Multipurpose engineer with a broad skill-set, a team-first attitude, and a commitment to excellence looking for an opportunity to join a high-performing organization supporting open-source technology. Interested in solving health needs and remote education during the pandemic.
Play Hard
Be Present
Forgive
Remember
The chart below was created in Python using plotly express for static hosting. The data comes from iPhone Healthkit and Google Fit sources. I’m curious how these companies define and evaluate fitness data and ultimately the narrative of our daily lives. How accurate are they? So far it appears they are similar in terms of steps and distance.
We can understand what a step is, but what about going up/down stairs and how does it compare to walking, jogging, running or biking? And what about move minutes and heart minutes?
Look closely at the Spring of 2020 when SF schools closed due to COVID, my kids stayed mostly inside the house but started to venture out more in the summer when mask wearing became more comfortable and familiar.
A simple Swift app scrapes the Healthit info and sends it (using amplify) to an AWS S3 bucket. If the Django site is provisioned on AWS, lambda functions will insert the data into an RDS MySql db; otherwise, I’ll grab from the bucket, manage the db locally and create the plotly derived html files. In general, the iPhone monitoring is always on.
My Swiss army knife, an android app “GetOut” that I’ve been slowly developing, will only monitor my gps and fitness when I want. I can select any Journal entry and follow the same process to make this data available on the cloud or locally.
The above journal routes “Delores Run” and “Ft Funston Dog Walk” are generated below using Plotly express and Mapbox line/scatter chart types.